In [1]:
import pandas as pd
import missingno as msn
import matplotlib.pyplot as plt
import numpy as np
from typing import Dict, List, Tuple
import math
数据观察¶
In [2]:
df_train = pd.read_csv("train.csv")
display(df_train.head())
| Id | MSSubClass | MSZoning | LotFrontage | LotArea | Street | Alley | LotShape | LandContour | Utilities | ... | PoolArea | PoolQC | Fence | MiscFeature | MiscVal | MoSold | YrSold | SaleType | SaleCondition | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 60 | RL | 65.0 | 8450 | Pave | NaN | Reg | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 2 | 2008 | WD | Normal | 208500 |
| 1 | 2 | 20 | RL | 80.0 | 9600 | Pave | NaN | Reg | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 5 | 2007 | WD | Normal | 181500 |
| 2 | 3 | 60 | RL | 68.0 | 11250 | Pave | NaN | IR1 | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 9 | 2008 | WD | Normal | 223500 |
| 3 | 4 | 70 | RL | 60.0 | 9550 | Pave | NaN | IR1 | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 2 | 2006 | WD | Abnorml | 140000 |
| 4 | 5 | 60 | RL | 84.0 | 14260 | Pave | NaN | IR1 | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 12 | 2008 | WD | Normal | 250000 |
5 rows × 81 columns
删除空缺值过多的列¶
- 部分列在训练集和测试集中空缺值很多，将这些列删除
- 空缺率大于30%的将进行列删除
In [3]:
##输出除了Id列全部是缺失值的行
empty_rows = df_train[df_train.drop(columns=['Id']).isnull().all(axis=1)]
print(empty_rows)
Empty DataFrame Columns: [Id, MSSubClass, MSZoning, LotFrontage, LotArea, Street, Alley, LotShape, LandContour, Utilities, LotConfig, LandSlope, Neighborhood, Condition1, Condition2, BldgType, HouseStyle, OverallQual, OverallCond, YearBuilt, YearRemodAdd, RoofStyle, RoofMatl, Exterior1st, Exterior2nd, MasVnrType, MasVnrArea, ExterQual, ExterCond, Foundation, BsmtQual, BsmtCond, BsmtExposure, BsmtFinType1, BsmtFinSF1, BsmtFinType2, BsmtFinSF2, BsmtUnfSF, TotalBsmtSF, Heating, HeatingQC, CentralAir, Electrical, 1stFlrSF, 2ndFlrSF, LowQualFinSF, GrLivArea, BsmtFullBath, BsmtHalfBath, FullBath, HalfBath, BedroomAbvGr, KitchenAbvGr, KitchenQual, TotRmsAbvGrd, Functional, Fireplaces, FireplaceQu, GarageType, GarageYrBlt, GarageFinish, GarageCars, GarageArea, GarageQual, GarageCond, PavedDrive, WoodDeckSF, OpenPorchSF, EnclosedPorch, 3SsnPorch, ScreenPorch, PoolArea, PoolQC, Fence, MiscFeature, MiscVal, MoSold, YrSold, SaleType, SaleCondition, SalePrice] Index: [] [0 rows x 81 columns]
In [4]:
df_test = pd.read_csv("test.csv")
fig = plt.figure(dpi=500, figsize=(40, 20))
ax1 = fig.add_subplot(121)
ax1.set_title('train')
ax2 = fig.add_subplot(122)
ax2.set_title('test')
msn.bar(df_train, ax=ax1)
msn.bar(df_test, ax=ax2)
Out[4]:
<Axes: title={'center': 'test'}>
In [5]:
delcols = df_train.isnull().sum().sort_values(ascending=False)
delcols = delcols/len(df_train)
print("空缺率:\n", delcols[:10])
空缺率: PoolQC 0.995205 MiscFeature 0.963014 Alley 0.937671 Fence 0.807534 MasVnrType 0.597260 FireplaceQu 0.472603 LotFrontage 0.177397 GarageYrBlt 0.055479 GarageCond 0.055479 GarageType 0.055479 dtype: float64
In [6]:
delcols = delcols[delcols>0.3].index.to_list()
print("会被删除的列:\n", delcols)
会被删除的列: ['PoolQC', 'MiscFeature', 'Alley', 'Fence', 'MasVnrType', 'FireplaceQu']
In [7]:
print(df_train.shape)
df_train = df_train.drop(columns=delcols)
print(df_train.shape)
msn.bar(df_train)
(1460, 81) (1460, 75)
Out[7]:
<Axes: >
根据列的数据类型划分连续型属性和离散型属性¶
In [8]:
p = df_train.dtypes.to_dict()
dtype_col:Dict[str, List[str]] = dict()
for ele in set(p.values()):
temp = []
ele = str(ele)
for key in p.keys():
if p[key] == ele:
temp.append(key)
dtype_col[ele] = temp
print(ele, len(temp), temp)
object 37 ['MSZoning', 'Street', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'SaleType', 'SaleCondition'] int64 35 ['Id', 'MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold', 'SalePrice'] float64 3 ['LotFrontage', 'MasVnrArea', 'GarageYrBlt']
In [9]:
type_col:Dict[str, List[str]] = dict()
type_col['continue'] = dtype_col['int64'] + dtype_col['float64']
type_col['discrete'] = dtype_col['object']
print(len(type_col['continue']), type_col['continue'])
print(len(type_col['discrete']), type_col['discrete'])
38 ['Id', 'MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold', 'SalePrice', 'LotFrontage', 'MasVnrArea', 'GarageYrBlt'] 37 ['MSZoning', 'Street', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'SaleType', 'SaleCondition']
数据预处理¶
连续型属性¶
画图¶
In [10]:
draw = type_col['continue'].copy()
draw.remove('SalePrice')
draw.remove('Id')
print(draw)
print(len(draw))
print(len(draw))
f = plt.figure(figsize=(20, 20))
for i in range(len(draw)):
ax = f.add_subplot(6,6,i+1)
df_train.plot(x=draw[i], y ='SalePrice', kind='scatter', ax=ax)
ax.set_title(draw[i])
f.tight_layout()
['MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold', 'LotFrontage', 'MasVnrArea', 'GarageYrBlt'] 36 36
离散型属性¶
In [11]:
for ele in type_col['discrete']:
print(ele, df_train[ele].value_counts().to_dict())
MSZoning {'RL': 1151, 'RM': 218, 'FV': 65, 'RH': 16, 'C (all)': 10}
Street {'Pave': 1454, 'Grvl': 6}
LotShape {'Reg': 925, 'IR1': 484, 'IR2': 41, 'IR3': 10}
LandContour {'Lvl': 1311, 'Bnk': 63, 'HLS': 50, 'Low': 36}
Utilities {'AllPub': 1459, 'NoSeWa': 1}
LotConfig {'Inside': 1052, 'Corner': 263, 'CulDSac': 94, 'FR2': 47, 'FR3': 4}
LandSlope {'Gtl': 1382, 'Mod': 65, 'Sev': 13}
Neighborhood {'NAmes': 225, 'CollgCr': 150, 'OldTown': 113, 'Edwards': 100, 'Somerst': 86, 'Gilbert': 79, 'NridgHt': 77, 'Sawyer': 74, 'NWAmes': 73, 'SawyerW': 59, 'BrkSide': 58, 'Crawfor': 51, 'Mitchel': 49, 'NoRidge': 41, 'Timber': 38, 'IDOTRR': 37, 'ClearCr': 28, 'StoneBr': 25, 'SWISU': 25, 'MeadowV': 17, 'Blmngtn': 17, 'BrDale': 16, 'Veenker': 11, 'NPkVill': 9, 'Blueste': 2}
Condition1 {'Norm': 1260, 'Feedr': 81, 'Artery': 48, 'RRAn': 26, 'PosN': 19, 'RRAe': 11, 'PosA': 8, 'RRNn': 5, 'RRNe': 2}
Condition2 {'Norm': 1445, 'Feedr': 6, 'Artery': 2, 'RRNn': 2, 'PosN': 2, 'PosA': 1, 'RRAn': 1, 'RRAe': 1}
BldgType {'1Fam': 1220, 'TwnhsE': 114, 'Duplex': 52, 'Twnhs': 43, '2fmCon': 31}
HouseStyle {'1Story': 726, '2Story': 445, '1.5Fin': 154, 'SLvl': 65, 'SFoyer': 37, '1.5Unf': 14, '2.5Unf': 11, '2.5Fin': 8}
RoofStyle {'Gable': 1141, 'Hip': 286, 'Flat': 13, 'Gambrel': 11, 'Mansard': 7, 'Shed': 2}
RoofMatl {'CompShg': 1434, 'Tar&Grv': 11, 'WdShngl': 6, 'WdShake': 5, 'Metal': 1, 'Membran': 1, 'Roll': 1, 'ClyTile': 1}
Exterior1st {'VinylSd': 515, 'HdBoard': 222, 'MetalSd': 220, 'Wd Sdng': 206, 'Plywood': 108, 'CemntBd': 61, 'BrkFace': 50, 'WdShing': 26, 'Stucco': 25, 'AsbShng': 20, 'BrkComm': 2, 'Stone': 2, 'AsphShn': 1, 'ImStucc': 1, 'CBlock': 1}
Exterior2nd {'VinylSd': 504, 'MetalSd': 214, 'HdBoard': 207, 'Wd Sdng': 197, 'Plywood': 142, 'CmentBd': 60, 'Wd Shng': 38, 'Stucco': 26, 'BrkFace': 25, 'AsbShng': 20, 'ImStucc': 10, 'Brk Cmn': 7, 'Stone': 5, 'AsphShn': 3, 'Other': 1, 'CBlock': 1}
ExterQual {'TA': 906, 'Gd': 488, 'Ex': 52, 'Fa': 14}
ExterCond {'TA': 1282, 'Gd': 146, 'Fa': 28, 'Ex': 3, 'Po': 1}
Foundation {'PConc': 647, 'CBlock': 634, 'BrkTil': 146, 'Slab': 24, 'Stone': 6, 'Wood': 3}
BsmtQual {'TA': 649, 'Gd': 618, 'Ex': 121, 'Fa': 35}
BsmtCond {'TA': 1311, 'Gd': 65, 'Fa': 45, 'Po': 2}
BsmtExposure {'No': 953, 'Av': 221, 'Gd': 134, 'Mn': 114}
BsmtFinType1 {'Unf': 430, 'GLQ': 418, 'ALQ': 220, 'BLQ': 148, 'Rec': 133, 'LwQ': 74}
BsmtFinType2 {'Unf': 1256, 'Rec': 54, 'LwQ': 46, 'BLQ': 33, 'ALQ': 19, 'GLQ': 14}
Heating {'GasA': 1428, 'GasW': 18, 'Grav': 7, 'Wall': 4, 'OthW': 2, 'Floor': 1}
HeatingQC {'Ex': 741, 'TA': 428, 'Gd': 241, 'Fa': 49, 'Po': 1}
CentralAir {'Y': 1365, 'N': 95}
Electrical {'SBrkr': 1334, 'FuseA': 94, 'FuseF': 27, 'FuseP': 3, 'Mix': 1}
KitchenQual {'TA': 735, 'Gd': 586, 'Ex': 100, 'Fa': 39}
Functional {'Typ': 1360, 'Min2': 34, 'Min1': 31, 'Mod': 15, 'Maj1': 14, 'Maj2': 5, 'Sev': 1}
GarageType {'Attchd': 870, 'Detchd': 387, 'BuiltIn': 88, 'Basment': 19, 'CarPort': 9, '2Types': 6}
GarageFinish {'Unf': 605, 'RFn': 422, 'Fin': 352}
GarageQual {'TA': 1311, 'Fa': 48, 'Gd': 14, 'Ex': 3, 'Po': 3}
GarageCond {'TA': 1326, 'Fa': 35, 'Gd': 9, 'Po': 7, 'Ex': 2}
PavedDrive {'Y': 1340, 'N': 90, 'P': 30}
SaleType {'WD': 1267, 'New': 122, 'COD': 43, 'ConLD': 9, 'ConLI': 5, 'ConLw': 5, 'CWD': 4, 'Oth': 3, 'Con': 2}
SaleCondition {'Normal': 1198, 'Partial': 125, 'Abnorml': 101, 'Family': 20, 'Alloca': 12, 'AdjLand': 4}
空缺值填充¶
- 对于连续型属性：采取列均值进行填充
- 对于离散型属性：采取用该列中最多出现的进行填充（Null值除外）
In [12]:
for colname in df_train.columns:
if df_train[colname].isna().sum() != 0:
print(df_train[colname].dtype, colname, '含有空值' ,df_train[colname].isna().sum())
if df_train[colname].dtype == 'object':
df_train[colname] = df_train[colname].fillna(df_train[colname].mode()[0])
elif df_train[colname].dtype == 'int64' or df_train[colname].dtype == 'float64':
df_train[colname] = df_train[colname].fillna(df_train[colname].mean())
else:
raise ValueError("未知数据类型")
float64 LotFrontage 含有空值 259 float64 MasVnrArea 含有空值 8 object BsmtQual 含有空值 37 object BsmtCond 含有空值 37 object BsmtExposure 含有空值 38 object BsmtFinType1 含有空值 37 object BsmtFinType2 含有空值 38 object Electrical 含有空值 1 object GarageType 含有空值 81 float64 GarageYrBlt 含有空值 81 object GarageFinish 含有空值 81 object GarageQual 含有空值 81 object GarageCond 含有空值 81
In [13]:
print("填充之后的df_train")
msn.matrix(df_train)
填充之后的df_train
Out[13]:
<Axes: >
删除df_train中Id字段，划分属性集合与最终结果SalePrice¶
- 也对连续型属性和离散型属性的字典进行修改
In [14]:
X_train, y_train = df_train.drop(columns=['SalePrice', 'Id']), df_train['SalePrice']
In [15]:
print(type_col['continue'])
type_col['continue'].remove('SalePrice')
type_col['continue'].remove('Id')
print(type_col['continue'])
['Id', 'MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold', 'SalePrice', 'LotFrontage', 'MasVnrArea', 'GarageYrBlt'] ['MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold', 'LotFrontage', 'MasVnrArea', 'GarageYrBlt']
对于连续型（数值型）数据进行数据标准化¶
In [16]:
from sklearn.preprocessing import StandardScaler
In [17]:
scaler = StandardScaler()
X_train[type_col['continue']] = scaler.fit_transform(X_train[type_col['continue']])
display(X_train[type_col['continue']].head())
| MSSubClass | LotArea | OverallQual | OverallCond | YearBuilt | YearRemodAdd | BsmtFinSF1 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | ... | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | MiscVal | MoSold | YrSold | LotFrontage | MasVnrArea | GarageYrBlt | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.073375 | -0.207142 | 0.651479 | -0.517200 | 1.050994 | 0.878668 | 0.575425 | -0.288653 | -0.944591 | -0.459303 | ... | -0.359325 | -0.116339 | -0.270208 | -0.068692 | -0.087688 | -1.599111 | 0.138777 | -0.229372 | 0.511418 | 1.021157 |
| 1 | -0.872563 | -0.091886 | -0.071836 | 2.179628 | 0.156734 | -0.429577 | 1.171992 | -0.288653 | -0.641228 | 0.466465 | ... | -0.359325 | -0.116339 | -0.270208 | -0.068692 | -0.087688 | -0.489110 | -0.614439 | 0.451936 | -0.574410 | -0.104483 |
| 2 | 0.073375 | 0.073480 | 0.651479 | -0.517200 | 0.984752 | 0.830215 | 0.092907 | -0.288653 | -0.301643 | -0.313369 | ... | -0.359325 | -0.116339 | -0.270208 | -0.068692 | -0.087688 | 0.990891 | 0.138777 | -0.093110 | 0.323060 | 0.937776 |
| 3 | 0.309859 | -0.096897 | 0.651479 | -0.517200 | -1.863632 | -0.720298 | -0.499274 | -0.288653 | -0.061670 | -0.687324 | ... | 4.092524 | -0.116339 | -0.270208 | -0.068692 | -0.087688 | -1.599111 | -1.367655 | -0.456474 | -0.574410 | 0.812705 |
| 4 | 0.073375 | 0.375148 | 1.374795 | -0.517200 | 0.951632 | 0.733308 | 0.463568 | -0.288653 | -0.174865 | 0.199680 | ... | -0.359325 | -0.116339 | -0.270208 | -0.068692 | -0.087688 | 2.100892 | 0.138777 | 0.633618 | 1.364570 | 0.896086 |
5 rows × 36 columns
对于离散型属性采取onehot编码¶
In [18]:
from sklearn.preprocessing import OneHotEncoder
In [19]:
encoder = OneHotEncoder(sparse_output=False, handle_unknown='ignore')
encoded_discrete_arr = encoder.fit_transform(X_train[type_col['discrete']])
encoded_discrete_df = pd.DataFrame(encoded_discrete_arr, columns=encoder.get_feature_names_out(type_col['discrete']))
X_train = pd.concat([X_train.drop(columns=type_col['discrete']), encoded_discrete_df], axis=1)
数据预处理与特征工程之后的X_train¶
In [20]:
display(X_train.head())
print(X_train.shape)
| MSSubClass | LotFrontage | LotArea | OverallQual | OverallCond | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | BsmtFinSF2 | ... | SaleType_ConLw | SaleType_New | SaleType_Oth | SaleType_WD | SaleCondition_Abnorml | SaleCondition_AdjLand | SaleCondition_Alloca | SaleCondition_Family | SaleCondition_Normal | SaleCondition_Partial | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.073375 | -0.229372 | -0.207142 | 0.651479 | -0.517200 | 1.050994 | 0.878668 | 0.511418 | 0.575425 | -0.288653 | ... | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 1 | -0.872563 | 0.451936 | -0.091886 | -0.071836 | 2.179628 | 0.156734 | -0.429577 | -0.574410 | 1.171992 | -0.288653 | ... | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 2 | 0.073375 | -0.093110 | 0.073480 | 0.651479 | -0.517200 | 0.984752 | 0.830215 | 0.323060 | 0.092907 | -0.288653 | ... | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 3 | 0.309859 | -0.456474 | -0.096897 | 0.651479 | -0.517200 | -1.863632 | -0.720298 | -0.574410 | -0.499274 | -0.288653 | ... | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4 | 0.073375 | 0.633618 | 0.375148 | 1.374795 | -0.517200 | 0.951632 | 0.733308 | 1.364570 | 0.463568 | -0.288653 | ... | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
5 rows × 266 columns
(1460, 266)
评估函数¶
- 即预测值和真实值之间的误差计算方法
- 参照网站上的要求
In [21]:
def cal_acc(real:list, pred:list)->float:
if len(real) != len(pred):
raise ValueError("输入的长度不一致")
N = len(real)
summary = sum([(math.log(p)-math.log(r))**2 for p, r in zip(pred, real)])
rmse = (summary/N)**0.5
return rmse
模型训练¶
- 使用的方法主要为随机森林
In [22]:
from sklearn.ensemble import RandomForestRegressor
In [23]:
model = RandomForestRegressor(n_estimators=100, random_state=13)
In [24]:
model.fit(X_train, y_train)
Out[24]:
RandomForestRegressor(random_state=13)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RandomForestRegressor(random_state=13)
In [25]:
y_train_pred = model.predict(X_train)
print("在训练集上的误差", cal_acc(y_train.to_list(), y_train_pred))
在训练集上的误差 0.05876237258950698
模型预测¶
对测试集数据进行与训练集数据一样的预处理¶
删除空缺值过多的列¶
In [26]:
df_test = pd.read_csv('test.csv')
df_test = df_test.drop(columns=delcols)
空缺值填补¶
In [27]:
for colname in df_test.columns:
if df_test[colname].isna().sum() != 0:
print(df_test[colname].dtype, colname, '含有空值' ,df_test[colname].isna().sum())
if df_test[colname].dtype == 'object':
df_test[colname] = df_test[colname].fillna(df_test[colname].mode()[0])
elif df_test[colname].dtype == 'int64' or df_test[colname].dtype == 'float64':
df_test[colname] = df_test[colname].fillna(df_test[colname].mean())
else:
raise ValueError("未知数据类型")
object MSZoning 含有空值 4 float64 LotFrontage 含有空值 227 object Utilities 含有空值 2 object Exterior1st 含有空值 1 object Exterior2nd 含有空值 1 float64 MasVnrArea 含有空值 15 object BsmtQual 含有空值 44 object BsmtCond 含有空值 45 object BsmtExposure 含有空值 44 object BsmtFinType1 含有空值 42 float64 BsmtFinSF1 含有空值 1 object BsmtFinType2 含有空值 42 float64 BsmtFinSF2 含有空值 1 float64 BsmtUnfSF 含有空值 1 float64 TotalBsmtSF 含有空值 1 float64 BsmtFullBath 含有空值 2 float64 BsmtHalfBath 含有空值 2 object KitchenQual 含有空值 1 object Functional 含有空值 2 object GarageType 含有空值 76 float64 GarageYrBlt 含有空值 78 object GarageFinish 含有空值 78 float64 GarageCars 含有空值 1 float64 GarageArea 含有空值 1 object GarageQual 含有空值 78 object GarageCond 含有空值 78 object SaleType 含有空值 1
In [28]:
msn.matrix(df_test)
Out[28]:
<Axes: >
对于连续型属性数据标准化¶
In [29]:
X_test = df_test.copy().drop(columns=["Id"])
In [30]:
X_test[type_col['continue']] = scaler.transform(X_test[type_col['continue']])
In [31]:
display(X_test[type_col['continue']].head())
| MSSubClass | LotArea | OverallQual | OverallCond | YearBuilt | YearRemodAdd | BsmtFinSF1 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | ... | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | MiscVal | MoSold | YrSold | LotFrontage | MasVnrArea | GarageYrBlt | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.872563 | 0.110763 | -0.795151 | 0.381743 | -0.340077 | -1.156380 | 0.053428 | 0.604293 | -0.672923 | -0.400017 | ... | -0.359325 | -0.116339 | 1.882709 | -0.068692 | -0.087688 | -0.119110 | 1.64521 | 0.451936 | -0.574410 | -0.729838 |
| 1 | -0.872563 | 0.375850 | -0.071836 | 0.381743 | -0.439440 | -1.301740 | 1.051363 | -0.288653 | -0.365032 | 0.619239 | ... | -0.359325 | -0.116339 | -0.270208 | -0.068692 | 25.116309 | -0.119110 | 1.64521 | 0.497357 | 0.023903 | -0.854909 |
| 2 | 0.073375 | 0.332053 | -0.795151 | -0.517200 | 0.852269 | 0.636400 | 0.761852 | -0.288653 | -0.974021 | -0.295127 | ... | -0.359325 | -0.116339 | -0.270208 | -0.068692 | -0.087688 | -1.229111 | 1.64521 | 0.179413 | -0.574410 | 0.771015 |
| 3 | 0.073375 | -0.054002 | -0.071836 | 0.381743 | 0.885390 | 0.636400 | 0.347326 | -0.288653 | -0.550672 | -0.299687 | ... | -0.359325 | -0.116339 | -0.270208 | -0.068692 | -0.087688 | -0.119110 | 1.64521 | 0.361095 | -0.463612 | 0.812705 |
| 4 | 1.492282 | -0.552407 | 1.374795 | -0.517200 | 0.686666 | 0.345679 | -0.396190 | -0.288653 | 1.018211 | 0.507509 | ... | -0.359325 | -0.116339 | 2.313293 | -0.068692 | -0.087688 | -1.969111 | 1.64521 | -1.228623 | -0.574410 | 0.562563 |
5 rows × 36 columns
对于离散属性onehot编码¶
In [32]:
encoded_discrete_arr = encoder.transform(X_test[type_col['discrete']])
encoded_discrete_df = pd.DataFrame(encoded_discrete_arr, columns=encoder.get_feature_names_out(type_col['discrete']))
X_test = pd.concat([X_test.drop(columns=type_col['discrete']), encoded_discrete_df], axis=1)
display(X_test.head())
| MSSubClass | LotFrontage | LotArea | OverallQual | OverallCond | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | BsmtFinSF2 | ... | SaleType_ConLw | SaleType_New | SaleType_Oth | SaleType_WD | SaleCondition_Abnorml | SaleCondition_AdjLand | SaleCondition_Alloca | SaleCondition_Family | SaleCondition_Normal | SaleCondition_Partial | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.872563 | 0.451936 | 0.110763 | -0.795151 | 0.381743 | -0.340077 | -1.156380 | -0.574410 | 0.053428 | 0.604293 | ... | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 1 | -0.872563 | 0.497357 | 0.375850 | -0.071836 | 0.381743 | -0.439440 | -1.301740 | 0.023903 | 1.051363 | -0.288653 | ... | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 2 | 0.073375 | 0.179413 | 0.332053 | -0.795151 | -0.517200 | 0.852269 | 0.636400 | -0.574410 | 0.761852 | -0.288653 | ... | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 3 | 0.073375 | 0.361095 | -0.054002 | -0.071836 | 0.381743 | 0.885390 | 0.636400 | -0.463612 | 0.347326 | -0.288653 | ... | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 4 | 1.492282 | -1.228623 | -0.552407 | 1.374795 | -0.517200 | 0.686666 | 0.345679 | -0.574410 | -0.396190 | -0.288653 | ... | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
5 rows × 266 columns
验证训练集和测试集在预处理之后对应列是否相同¶
In [33]:
xtrainc = X_train.columns.to_list()
xtestc = X_test.columns.to_list()
if len(xtrainc) != len(xtestc):
raise ValueError("表结构不同")
if len(xtrainc) == len(xtestc):
for i in range(len(xtrainc)):
if xtrainc[i] != xtestc[i]:
raise ValueError("表结构不同")
进行预测¶
In [34]:
y_test_pred = model.predict(X_test)
In [35]:
y_test_id = df_test['Id'].to_list()
if len(y_test_id)!=len(y_test_pred):
raise ValueError('')
In [36]:
res = pd.DataFrame(data={
'Id': y_test_id,
'SalePrice': y_test_pred
})
res.to_csv(f'res.csv', index=False)
res
Out[36]:
| Id | SalePrice | |
|---|---|---|
| 0 | 1461 | 128350.00 |
| 1 | 1462 | 156391.32 |
| 2 | 1463 | 181188.60 |
| 3 | 1464 | 180211.35 |
| 4 | 1465 | 196018.89 |
| ... | ... | ... |
| 1454 | 2915 | 87086.00 |
| 1455 | 2916 | 87928.42 |
| 1456 | 2917 | 151970.87 |
| 1457 | 2918 | 116225.50 |
| 1458 | 2919 | 229271.32 |
1459 rows × 2 columns